Triple
T13335890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geoje-dong |
E317690
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Yeonje District |
E36486
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Yeonje District | Statement: [Geoje-dong, locatedIn, Yeonje District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeonje District Context triple: [Geoje-dong, locatedIn, Yeonje District]
-
A.
Yeonje District
chosen
Yeonje District is an urban administrative district located in the central area of Busan, South Korea, known for its residential neighborhoods and transportation links.
-
B.
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
-
C.
Gwangjin District
Gwangjin District is an eastern borough of Seoul, South Korea, known for its universities, shopping areas, and location along the Han River.
-
D.
Seongbuk District
Seongbuk District is a residential and educational borough in northern Seoul, South Korea, known for its universities, cultural sites, and traditional neighborhoods.
-
E.
Taesong District
Taesong District is an administrative district of Pyongyang, North Korea, known for hosting major educational and cultural institutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99d00b75c8190af98784c7df904c8 |
completed | April 11, 2026, 12:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd27f43bf081908dea65dc05f7c1a2 |
completed | May 8, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:30 p.m.